Real-world unified denoising for multi-organ fast MRI: a large-scale prospective validation.
Authors
Affiliations (13)
Affiliations (13)
- School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China.
- Department of Radiology, Shenzhen Nanshan People's Hospital, Shenzhen University, Shenzhen, China.
- Department of Radiology, Longgang Central Hospital, Shenzhen, China.
- Department of Radiology, Panyu Second People's Hospital, Guangzhou, China.
- Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China.
- Department of Radiology, Southern University of Science and Technology Hospital, Shenzhen, China.
- Department of Radiology, Shenzhen FuYong People's Hospital, Shenzhen, China.
- Department of Radiology, Shenzhen Bao'an Songgang People's Hospital, Shenzhen, China.
- School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China. [email protected].
- School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China. [email protected].
- Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China. [email protected].
- Shanghai Clinical Research and Trial Center, Shanghai, China. [email protected].
- Department of Radiology, Shenzhen Nanshan People's Hospital, Shenzhen University, Shenzhen, China. [email protected].
Abstract
Lengthy acquisition time remains a key bottleneck for the widespread use of MRI in clinics. While accelerated MRI can reduce scan duration, it often introduces increased noise, compromising image quality and diagnostic reliability. In this study, we present a unified deep learning-based denoising model for multi-organ accelerated MRI, designed to operate directly on reconstructed images from commercial MRI systems. Our model was trained on a prospectively collected, large-scale real-world dataset comprising 148,930 noisy-clean image pairs from six clinical centers and four major MRI vendors, spanning six organs and 96 MRI protocols. On a test set of 20,143 real-world image pairs, our model consistently outperforms state-of-the-art denoising methods. Importantly, downstream evaluation using tissue segmentation demonstrates a 7.05% improvement in Dice score across multiple organs compared to noisy images. The model further generalizes effectively to 46,870 external clinical images from four independent cohorts, highlighting its robustness across various scanners and acquisition protocols. To assess clinical utility, two experienced radiologists conducted blinded evaluations across multiple organs, focusing on overall image quality, diagnostic confidence, and disease diagnosis. The denoised images retained high visual fidelity and yielded diagnostic performance equivalent to clean images even with acceleration factor of 3× compared to clinical scanning setup, such that many acquisitions can be completed within one minute. This unified MRI denoising model holds great potential for various clinical applications.